Real-time projects · One-to-one mentorship · Hyderabad & Online
MLOps Training in Hyderabad
Learn to build, deploy, monitor and scale machine learning models in production. Our MLOps training in Hyderabad covers Docker, Kubernetes, CI/CD, MLflow, Airflow and cloud platforms like AWS and Azure, with real-time projects, one-to-one mentorship and internship support until you are placed.
✔ Real-time projects
✔ Internship till you are placed
✔ One-to-one mentorship
✔ Placement support and certification guidance
4.9★ Google rating (51 reviews)
400+ learners trained
3 months live training
₹20,000 online fee
Next batch starts Monday, 5 October 2026 · Morning 10:00 AM & evening 7:00 PM IST batches · Classroom near JNTU Metro or live online
Course at a glance
Next batch Mon, 5 Oct 2026 · 10 AM & 7 PM
Fee ₹20,000 online · ₹25,000 classroom
Duration 3 months, live classes
Modes Classroom, online, corporate
Trainer Dinesh Tunguturi
Tools Docker, Kubernetes, MLflow, AWS
MLOps Training in Hyderabad: Batch Details & Fees
Everything you need to plan your enrolment in one place. New batches run in both classroom and live online formats.
Next batch
Mon, 5 October 2026
Morning 10:00 AM & evening 7:00 PM IST
Course fee
₹20,000 online
₹25,000 classroom
Course duration
3 months
Live, trainer-led classes
Training modes
Classroom training (Hyderabad)
Online & corporate training
Trainer
Dinesh Tunguturi
In industry since 2016
Location
JNTU Metro Station, Kukatpally
Hyderabad 500072
Call +91 98850 44555 · Email genaimasters@gmail.com · Chat on WhatsApp for demo class details
What is the MLOps training at Generative AI Masters?
The MLOps training at Generative AI Masters is a 3-month, trainer-led course in Hyderabad that teaches you to take machine learning models from notebook to production. You learn the ML project lifecycle, pipelines and artifacts, orchestrators and artifact stores, deployment with Docker, Kubernetes, FastAPI and TensorFlow Serving, CI/CD for ML, experiment tracking with MLflow and monitoring with Prometheus and Grafana.
Classes run in a classroom near JNTU Metro Station, Kukatpally, live online or as corporate training. The fee is ₹20,000 online or ₹25,000 for classroom training, and the next batch starts on 5 October 2026. It suits both beginners with basic Python and ML knowledge and working professionals. For LLMs and Generative AI, see our Generative AI training in Hyderabad.
At a glance
✔ For beginners with basic Python & ML
✔ Duration: 3 months of live classes
✔ Fee: ₹20,000 online · ₹25,000 classroom
✔ Tools: Docker, Kubernetes, Jenkins, MLflow
✔ Cloud: AWS and Azure
✔ Placement support included
What is MLOps?
✔ MLOps stands for Machine Learning Operations. It combines the processes of machine learning and DevOps to make it easier to build, test, deploy and manage ML models in production environments.
✔ It focuses on automating and improving the process of deploying machine learning models, making it quicker, more reliable and easier to manage.
✔ MLOps manages the full machine learning workflow, from preparing the data and training the model to deploying it and monitoring its performance after deployment.
✔ It helps data scientists and IT operations teams work together more effectively through smoother communication and shared workflows.
✔ Continuous Integration and Continuous Delivery (CI/CD) are important parts of MLOps, automating testing, building and deploying models.
✔ MLOps makes it easier to update and scale models quickly and reliably, so they stay accurate as data and users grow.
✔ It involves monitoring models in production to detect problems like data drift and fix them promptly.
✔ The main goal of MLOps is to make machine learning models more dependable and simpler to maintain in real-time applications.
Why is MLOps Used?
✔ To make it easier and faster to put machine learning models into real use.
✔ To update models in a steady and trustworthy way.
✔ To watch models in production so problems are found and fixed quickly.
✔ To help data scientists and IT teams work together better.
✔ To keep adding and improving machine learning models.
✔ To make models work well even with many users at the same time.
✔ To manage changes in data and update models simply.
✔ To help machine learning systems run smoothly and without problems.
About MLOps
MLOps, short for Machine Learning Operations, is a practice that combines machine learning with DevOps to streamline and automate the deployment, monitoring and management of machine learning models in production. It bridges the gap between data scientists and IT operations, so models can be efficiently and reliably integrated into real-time applications.
MLOps covers the entire machine learning lifecycle, including data preparation, model training, deployment, monitoring and retraining, allowing organisations to continuously deliver and improve ML-driven solutions. Its primary goal is better collaboration between data science and operations teams, enabling faster experimentation, more reliable deployments and better scalability.
By applying DevOps principles such as continuous integration and continuous delivery (CI/CD), MLOps ensures models can be rapidly tested and deployed, with automated workflows reducing the risk of errors. It also emphasises monitoring models in production to detect issues like data drift or performance degradation, so teams can intervene and update models in time.
This approach not only accelerates the deployment of machine learning models but also ensures their long-term reliability and effectiveness in production environments.
Generative AI Masters in Hyderabad offers MLOps training designed to equip learners with the skills needed to operationalise machine learning models effectively. The course covers essential tools and practices like Kubernetes, Docker, Jenkins and cloud platforms such as AWS and Azure, so you gain hands-on experience automating and managing ML pipelines. With a curriculum that blends theory with real-world projects, it prepares both beginners and professionals to excel in the fast-growing field of MLOps, guided by expert instructors.
MLOps Course Curriculum 2026
Four modules take you from MLOps fundamentals to running and managing ML systems in production, with hands-on labs in each.
Module 1
Intro to MLOps
What is MLOps? Machine Learning Operations combines machine learning, DevOps and data engineering to manage, deploy and monitor ML models in production, with automation, collaboration and faster delivery.
Different stages in MLOps: data collection, model training, model versioning, model deployment, monitoring and model retraining.
ML project lifecycle: define the problem, collect and prepare data, build the model, evaluate it, deploy it, then monitor and improve.
Job roles in MLOps: MLOps Engineer, ML Engineer, Data Engineer, DevOps Engineer (ML) and AI/ML Architect.
Module 2
Design and Development
Development stage of an ML workflow: where data scientists explore data, create features, and build, train and evaluate models through experimentation.
Pipelines and steps: repeatable, scalable sequences for data loading, preprocessing, training, evaluation and deployment prep.
Artifacts: outputs of each step such as processed datasets, trained models and evaluation metrics.
Materializers: how artifacts are saved and retrieved, for example saving a trained model as a .pkl file.
Parameters & settings: learning rate, batch size, retries and caching, for easy experimentation without editing code.
Module 3
Execution
Stacks & components: the complete environment to run an ML workflow, including orchestrator, artifact store and container runtime.
Orchestrators: schedule and manage pipeline runs in the right order with Airflow, Kubeflow, Prefect or ZenML.
Artifact stores: cloud (S3, GCS) or local storage for datasets, models and logs, for reproducibility and version tracking.
Flavors: the specific tool for each component, such as Apache Airflow, AWS S3 or MLflow, making stacks modular and customisable.
Module 4
Management
ML server infrastructure: on-premise or cloud (AWS, GCP, Azure) setups built for scalability, availability and performance.
Server deployment: serve predictions via REST APIs, batch jobs or streaming with Docker, Kubernetes, Flask, FastAPI and TensorFlow Serving.
Metadata tracking: model and dataset versions, training parameters, results and logs for debugging, auditing and reproducibility.
Collaboration: shared pipelines, access control, Git version control and project tracking across data science, ML and DevOps teams.
Dashboards: monitor model performance, data drift, training metrics and system health with Grafana, Prometheus, MLflow UI, Streamlit and ZenML.
Course Outline
01Introduction to MLOps concepts and their importance in machine learning.
02Core MLOps tools and technologies, including Docker and Kubernetes.
03Building and managing machine learning pipelines.
04Practical CI/CD for building and updating machine learning models.
05Deploying models to cloud platforms like AWS and Azure.
06Monitoring and maintaining models in production.
07Real-world projects applying MLOps concepts.
08Review of best practices and advanced MLOps topics.
Tools Covered in the MLOps Training
Docker
Packages ML applications so they run the same way in development, testing and production.
Kubernetes
Manages containerised applications, making it easier to deploy and scale ML models.
Jenkins
Automates building, testing and delivering machine learning models.
Git
Tracks changes in code and model settings.
Terraform
Creates and manages cloud resources automatically with code.
MLflow
Tracks experiments, manages model versions and helps deploy models.
Apache Airflow
Schedules and monitors tasks and pipelines in ML projects.
Prometheus and Grafana
Monitor and graph model performance and system health.
Kubeflow
Deploys and manages machine learning workflows on Kubernetes.
Skills Developed Post MLOps Training
Use tools like Docker & KubernetesPackage and run ML models easily across different systems.
Automate ML processesSet up systems that handle training, testing and updating models automatically.
Understand CI/CD for MLKeep ML projects running smoothly with automated testing and updates.
Work with cloud platformsDeploy ML models on AWS and Azure so they scale as needed.
Monitor models in productionMake sure deployed models keep performing well over time.
Handle data and features properlyManage data versions and engineer useful features.
Fix model issuesDiagnose and fix models that start performing poorly or behaving differently.
Work well with teamsCollaborate with data science and IT teams so projects move faster.
Why Choose Us for MLOps Training in Hyderabad
Expert Instructors
Training is led by industry professionals with strong hands-on experience in MLOps and machine learning, who share real-world knowledge, practical examples and continuous guidance.
Hands-On Experience
You learn by doing, working on real projects and examples that show how MLOps works in real life and build your confidence with MLOps tools and methods.
Complete Curriculum
A clear, organised course from basic to advanced topics, with hands-on practice using Docker, Kubernetes and cloud platforms.
Industry-Relevant Training
Designed around what companies are looking for, so you learn the in-demand skills and tools that lead to better MLOps job opportunities.
Personalized Support
Each student gets personal help with projects, career tips and support whenever they face difficulties during the course.
Strong Placement Support
Career advice, resume tips, interview support and company connections help students find good job opportunities in MLOps.
Flexible Learning Options
Online and offline classes let you choose what suits your schedule, so working professionals can learn without interrupting their jobs.
Positive Reviews and Success Stories
Many learners have moved forward in their MLOps careers after the training, and their feedback shows the program works.
Modes - MLOps Training in Hyderabad
Classroom Training
✔ Interactive face-to-face teaching
✔ Industry expert trainers
✔ Instant feedback
✔ Collaborative tasks
✔ Hands-on industry projects
✔ Group discussions
✔ Covers advanced topics
₹25,000 · near JNTU Metro, Kukatpally
Online Training
✔ Virtual learning sessions
✔ Daily session recordings
✔ Instructor support
✔ Interactive webinars
✔ Digital learning modules
✔ Online practical labs
✔ Flexible learning schedules
₹20,000 · live online
Corporate Training
✔ Customised training programs
✔ Daily recordings
✔ Interactive team development
✔ Expert instruction
✔ Industry-relevant content
✔ Performance monitoring
✔ On-site workshops
Custom quote for teams
Career Opportunities in MLOps
MLOps is one of the fastest-growing areas in tech, offering a wide range of career paths for professionals who can manage and optimise machine learning in real-world environments. With AI adoption increasing across industries, skilled MLOps professionals are in high demand. Here are some key job opportunities in MLOps:
MLOps EngineerDeploys, manages and scales ML models in production, bridging data science and IT operations.
Machine Learning Operations SpecialistAutomates ML workflows, maintains model performance and applies best practices.
Data EngineerBuilds and manages the data pipelines that feed ML models with reliable data.
DevOps EngineerIntegrates ML models into DevOps processes, CI/CD pipelines and deployment automation.
ML Infrastructure EngineerDesigns and manages the cloud and on-premise infrastructure for ML workloads.
AI Operations ManagerOversees the full ML operations lifecycle so models stay accurate and up to date.
Machine Learning ArchitectPlans scalable, reliable architecture for ML model deployment and integration.
Data Scientist with MLOps SkillsBuilds models and makes them production-ready and maintainable.
Cloud EngineerManages cloud infrastructure for secure, scalable ML deployment on AWS, Azure or GCP.
Model Monitoring SpecialistTracks live model performance, detects data drift and triggers corrective actions.
Key Points of MLOps Training
✔ Integrates machine learning with DevOps practices.
✔ Hands-on experience with Kubernetes, Docker and Jenkins.
✔ End-to-end machine learning pipeline automation.
✔ Continuous integration and continuous deployment (CI/CD) for ML models.
✔ Real-world projects to apply MLOps concepts.
✔ Master model monitoring and management.
✔ Data versioning and feature engineering for robust models.
✔ Training on cloud platforms like AWS and Azure.
✔ Suitable for both beginners and professionals.
✔ Expert instructors guide you through operationalising ML models.
Placement Support at Generative AI Masters
Learning is just the beginning. Our placement program guides you through every step of your job search so you can land the right MLOps or AI role with confidence:
✔ Career counselling: personalised guidance based on your skills, interests and goals.
✔ Resume building: highlight your training, projects, technical skills and certifications.
✔ Interview preparation: mock interviews, feedback and common technical and HR questions.
✔ Job search support: job listings, company referrals and placement leads through our partner network.
Companies That Hire
AMD Wipro Deloitte Genpact Citi TCS
MLOps Training in Hyderabad: Prerequisites & Pay Scale
4000+ Job Openings for MLOps
Prerequisites
You don't need to be an expert to join. Just a few basics will help you get the most out of the training:
✔ Basic machine learning knowledge: know a little about what models are and how they learn from data.
✔ Python programming: be comfortable writing simple Python code, since we use it throughout the course.
✔ Using Git (helpful, not required): if you haven't used Git before, we'll help you get started.
✔ Cloud or Docker experience (nice to have): AWS or Docker experience is a plus, but not necessary.
Don't worry if you're missing something. We provide support and extra resources to help you catch up.
Approximate Pay Scale for MLOps Engineers in India
Entry-Level MLOps Engineer (0–2 years): ₹6–10 lakhs per annum
Help set up and run ML models, automate simple ML tasks, use Docker, Git and CI/CD, work with data scientists and DevOps teams, and keep project files organised and documented.
Mid-Level MLOps Engineer (2–5 years): ₹12–18 lakhs per annum
Build and manage training and deployment pipelines, speed up model updates, manage systems on AWS, Azure or GCP, set up monitoring, and guide junior team members.
Senior MLOps Engineer (5+ years): ₹20–35 lakhs per annum
Lead the design of full MLOps systems, set best practices, oversee model tracking, updates and rollbacks, keep systems safe and compliant, and mentor others.
Lead or MLOps Architect (7+ years): ₹38–43 lakhs per annum
Plan the company's MLOps strategy, design large-scale ML systems, manage cross-functional teams and choose the right tools to support business goals.
Ranges are approximate market estimates and vary by city, company, skills and experience.
MLOps Market Trends
MLOps is quickly becoming one of the most in-demand fields in AI and data. As more companies adopt machine learning, they need skilled professionals to deploy, manage and scale ML models effectively.
Rapid growth and demandFrom healthcare to finance, companies actively seek MLOps experts to manage their AI systems.
Automation is keyAutomating ML workflows and model updates reduces manual work and errors.
Cloud + MLOps = scalabilityIntegrating MLOps with AWS, Azure and GCP makes systems scalable, efficient and cost-effective.
New tools and frameworksNew platforms tackle model monitoring, version control and real-time performance tracking.
Focus on monitoring and managementContinuous monitoring handles data drift and model degradation over time.
Rising job opportunitiesMLOps Engineer, Data Engineer, ML Infrastructure Specialist and AI Operations Manager roles are growing fast and pay well.
Bridging the gap between teamsMLOps professionals connect data science teams with IT/DevOps teams, which is crucial for AI success.
Generative AI Masters Achievements
400+Learners trained
60+Learners placed
Since 2016Trainer in industry
4.9★51 Google reviews
Generative AI Learners Testimonials
“The MLOps training in Hyderabad at Generative AI Masters was a game-changer for my career. The hands-on projects and expert guidance provided deep insights into managing and deploying machine learning models. I now feel confident handling complex MLOps tasks and have already seen the benefits in my current role.”Aditi Sharma
“Generative AI Masters' MLOps program exceeded my expectations. The course offered practical experience with essential tools and technologies, and the instructors were incredibly knowledgeable. This training gave me the skills needed to transition into an MLOps role and has significantly boosted my professional growth.”Divya Jha
“I chose Generative AI Masters for their MLOps training, and it was the best decision. The curriculum was comprehensive, covering everything from basic concepts to advanced techniques. The real-world projects were especially valuable in applying what I learned, and I'm now more prepared for the challenges in MLOps.”Srikanth
“The MLOps training in Hyderabad at Generative AI Masters provided a solid foundation in managing machine learning models effectively. The hands-on approach and the support from experienced instructors made a huge difference. I appreciated the focus on industry-relevant skills and the practical applications of MLOps.”Rahul
“Generative AI Masters offers exceptional MLOps training. The course was well-structured, and the practical projects helped me understand the intricacies of model deployment and management. The knowledge I gained has already opened up new opportunities in my career, and I highly recommend this program to anyone looking to specialize in MLOps.”Sai kiran
“Generative AI Masters provides outstanding MLOps training in Hyderabad. The course was expertly structured, and the hands-on projects gave me a deep understanding of model deployment and management. The knowledge I've gained has already unlocked new career opportunities, and I strongly recommend this program to anyone seeking to specialize in MLOps.”Ram
“The MLOps training at Generative AI Masters in Hyderabad was fantastic! Trainer Madhumathi made every concept easy to understand and shared real-world examples. The hands-on sessions were very helpful, and I feel confident about applying MLOps in my job. Thank you, Madhumathi, for your excellent guidance!”Raju
“I had a great learning experience with the MLOps training at Generative AI Masters. Madhumathi is an amazing trainer who explained everything clearly and answered all our questions patiently. The practical projects made the course even more useful. I recommend this training to anyone who wants to learn MLOps.”Padma
Your trainer
Trainer Details - MLOps Training in Hyderabad
Dinesh Tunguturi – Lead AI Trainer, in Industry Since 2016
Dinesh is a data scientist who has worked in industry since 2016: forecasting models and dashboards at Tata Motors, healthcare supply-chain analytics for e-Aushadhi at GA Digital, and machine-learning models on AWS for the Nasdaq Automated Investigator (anti-money-laundering) at Virtusa. Since 2024 he has been Senior Data Scientist at Brolly Software Solutions.
He has a special talent for making complex topics easy to understand and use for students from different backgrounds. Using practical experience and real-time examples, he helps students build strong skills and prepares them for success.
Today he builds production Generative AI systems, including a RAG content platform (LangChain, Chroma vector database, Gradio) and a multimodal content agent using the Groq API and LLaMA 4 Scout — the same tools and patterns learners use in class.
View Full Trainer Profile →MLOps Certification
Certifications in MLOps are valuable for professionals who want to validate their skills in managing machine learning operations. They demonstrate proficiency in deploying, monitoring and maintaining machine learning models, and can significantly enhance your career prospects.
Earning a certification usually involves passing an exam on key concepts, tools and best practices such as CI/CD and model monitoring. The training helps you build the hands-on skills these exams test. Here are some certifications for MLOps:
Microsoft Certified: Azure Data Scientist Associate
Focuses on managing machine learning models and data pipelines on Microsoft Azure, including deploying, managing and optimising ML solutions.
Google Professional Machine Learning Engineer
Demonstrates expertise in designing, building and deploying ML models on Google Cloud, with emphasis on scalability and performance.
AWS Certified Machine Learning – Specialty
Shows proficiency in deploying and managing ML models on AWS, covering model optimisation, deployment and maintenance.
AWS Certified Machine Learning Engineer – Associate
AWS's newer role-based certification focused on building, deploying and operating ML workloads, closely aligned with day-to-day MLOps work.
Certified MLOps Professional (CMOP)
Designed specifically for MLOps practitioners, focusing on best practices and tools for deployment, monitoring and lifecycle management.
MLOps Training in Hyderabad: FAQs
What is MLOps?
MLOps, or Machine Learning Operations, is a set of practices that combines machine learning with DevOps to streamline and automate the deployment, management and monitoring of machine learning models in production environments.
What is the MLOps training fee in Hyderabad?
The MLOps training fee at Generative AI Masters is ₹20,000 for live online training and ₹25,000 for classroom training near JNTU Metro Station, Kukatpally, Hyderabad.
When does the next MLOps batch start?
The next batch starts on Monday, 5 October 2026. Choose the morning batch (10:00 AM IST) or the evening batch (7:00 PM IST), in classroom or live online mode. Call +91 98850 44555 or message us on WhatsApp to reserve a seat.
What are the key benefits of MLOps training?
MLOps training builds skills in automating ML pipelines, managing model deployment, monitoring performance and integrating ML models with existing IT infrastructure, leading to better efficiency and scalability.
What prerequisites are needed for MLOps training?
A basic understanding of machine learning concepts, familiarity with Python and knowledge of version control systems like Git are recommended. Experience with cloud platforms or containerisation tools is helpful but not required.
How long does the MLOps training program last?
Our MLOps training runs for 3 months of live, trainer-led classes. In general, MLOps programs range from 3 to 4 months depending on depth and learning format.
What tools are covered in MLOps training?
The training covers Docker, Kubernetes, Jenkins, Git, Terraform, MLflow, Apache Airflow, Kubeflow, Prometheus and Grafana, and cloud platforms such as AWS, Azure and Google Cloud.
Is MLOps training suitable for beginners?
Yes. It suits beginners who have a basic understanding of machine learning and programming, and we provide support and extra resources to help you catch up on any gaps.
What career opportunities are available after completing MLOps training?
Roles include MLOps Engineer, Machine Learning Operations Specialist, Data Engineer, DevOps Engineer, ML Infrastructure Engineer and AI Operations Manager.
What is the average salary of an MLOps Engineer?
Salaries vary by skill set and experience. Recent reports suggest MLOps Engineers in India earn around ₹13,00,000 per annum on average, with entry-level roles typically starting at ₹6–10 lakhs.
Does Generative AI Masters offer placement assistance after training?
Yes. Placement assistance includes career counselling, resume building, interview preparation and access to job opportunities through our industry network. Contact Generative AI Masters for more details.
Are there any certification options available with the training?
The training helps you prepare for certifications such as Microsoft Certified: Azure Data Scientist Associate, Google Professional Machine Learning Engineer, AWS machine learning certifications and Certified MLOps Professional (CMOP).
Can I take MLOps training online?
Yes. Live online classes let you learn from anywhere, with virtual classrooms, recorded sessions and online resources, for ₹20,000.
What is MLOps used for?
MLOps is used to automate, monitor and manage machine learning workflows, ensuring smooth deployment, scalability and maintenance of ML models in production.
What is MLOps vs DevOps?
DevOps focuses on software development and operations. MLOps adds ML-specific tasks like data processing, model training and versioning on top of DevOps to support the ML lifecycle.
Is MLOps a good career?
Yes. As more companies use AI, there is a strong need for MLOps skills. It offers good salaries, career growth and the chance to work with the latest technologies.
What is CI and CD in MLOps?
CI (Continuous Integration) automates testing and versioning of ML code and models. CD (Continuous Delivery/Deployment) automates deploying models to production.
Is Kubernetes used in MLOps?
Yes. Kubernetes is commonly used to deploy, scale and manage ML workloads, especially in containerised environments.
What is MLOps certification?
MLOps certifications validate your skills in ML model deployment, automation and pipeline management. Examples include Google Cloud machine learning certifications, AWS machine learning certifications and Coursera, edX or Databricks MLOps courses.
What is the future of MLOps?
MLOps will become a core part of AI infrastructure, with more automation, better monitoring and tighter integration with data pipelines and edge computing.
What are the principles of MLOps?
Automation of pipelines; version control for data, models and code; reproducibility; continuous training and deployment; monitoring and governance; and collaboration across teams.
Does MLOps require coding?
Yes, but less than data science. Knowledge of Python, scripting and tools like Docker, Kubernetes and CI/CD pipelines is essential.
Other Relevant Courses
Launch Your MLOps Career. Next Batch: 5 October 2026
Join the MLOps training in Hyderabad with real-time projects, one-to-one mentorship and placement support. ₹20,000 online · ₹25,000 classroom near JNTU Metro, Kukatpally.
★ 4.9 from 51 Google reviews · 400+ learners trained · 60+ placed
Metro Pillar No: A689, JNTU Metro Station, 3rd Floor, Dr Atmaram Estates, beside Sri Bhramaramba Theatre, Hyder Nagar, Vasantha Nagar, Hyderabad, Telangana 500072 · Get directions · genaimasters@gmail.com